The Autoregressive metric for comparing time series models

Abstract

The Autoregressive metric was firstly introduced in 1983 as a tool for choosing a representative element from a large collection of time series and for clustering temporal data. The proposal has been extended to many contexts and has raised increasing interests in both time series methods and applications. The main results concerning this metric, its asymptotic distribution and some operational and comparative issues are presented. A discussion about the merits of this distance criterion and some caveats about its usage conclude the paper.